\chapter{Summary and Conclusion}
\label{chap:Summary and Conclusion}

\section{Achievements}
\label{Achievements}
There are four achievements.
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\textbf{Achievement One:} Implementation of different types of agents with different specifications that help in breaking down complex problems into simple tasks.
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\textbf{Achievement Two:} Implementation of different types of team work for the same agents which helps in creating multi-task agent teams that can solve different problems. 
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\textbf{Achievement Three:} Implementing a user working with agents creating a new teamwork between user and computer agents where they complete each others.
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\textbf{Achievement Four:} Implementation of smart agents that can collaborate with environments that can be manipulated any time by insertion function.

\clearpage
\section{Limitations}
\label{Limitations}
There are five limitations.
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\textbf{Limitation One:} Motion is done in 2D (x,z) coordinates, there is no motion in the y axis.
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\textbf{Limitation Two:} Resources and obstacles are mostly simple mathematical objects like cubes or spheres.
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\textbf{Limitation Three:} Enemies have no colony and don't attack the ants, thus making the game easy.
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\textbf{Limitation Four: }Agents lack self-learning algorithms, they need to inform the master (alarm ant) in order to know what to do.
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\textbf{Limitation Five:} User freedom is limited only with free motion and choosing between performing tasks or ignoring them.


\section{Future Research}
\label{Future Research}
This project has many future research topics.
\\

\textbf{Topic One:} More investigations and researches on different types of teamwork. Through history any problem in the world was solved by teamwork between humans. Therefore the more researches done on different types and categories of teamwork that can be resolved using agents, the more researches are close to solving more problems.
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\textbf{Topic Two:} Breaking complex problems into different tasks done by different agents. This topic opens gate towards a compare and contrast research  between diversity of agents versus complexity of agents. In this thesis, problems were split into 3 tasks and performed by 3 types of agents. Maybe having 7 tasks and 5 different agents is better, or perhaps having 2 tasks and 6 different agents is better. Reaching the best balance is indeed a research topic.
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\textbf{Topic Three:} Having different colonies with different ideologies and algorithms competing against each others. This opens a gate on finding best combination of algorithms and ways of implementation, by having different colonies compete against each other. 
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\textbf{Topic Four:} Computer agents versus user agent. This project also raised another topic which is the differences between user agent and computer agent performances. The computer agent showed a better performance form some attributes perspective. A future research can be made to try as much as possible to make the computer agent attributes that are below those of the user agent equal to each others or even better. This might help to create a computer agent that can surpass the user agent.
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\textbf{Topic Five:} User and agents complementary teamwork. It is true that this project had users and agents working together. However, the user was just one of the worker ants, and worked the same way like other worker ants with freedom in movement adding nothing mainly. This raised a topic of researching on making user agent another type of ant in the colony, that has unique skills and performs jobs only done by him. 

\section{Conclusion}
\label{Conclusion}
The more complex problems arise the more importance of high-quality AI solutions increases. Modern virtual simulations require more advanced approaches that can provide more realistic and effective behavior that are hard to achieve with traditional methods in complex environments.\cite{six} Having one agent solve the whole problem became almost impossible and thus the field of breaking the problem between agents arrived. The results of this study are important to those who study AI agents' teamwork. It contains different implementations for different agents with the same approach but different algorithms and methods working together to solve complex problems by breaking it into smaller tasks. It verifies an example of two types of teamwork carried by the same agents in different missions. Last but not least it also contains teamwork between the user and agents and compares the difference between their performance.

\section{Simulation figures}
\label{Simulation figures}
The following simulation figures include both computer agents and the user agent.
\clearpage
 The following figure shows agents gathering food from a resource, as well as other agents going back to the nest carrying food piles. 

\begin{figure}[htp]
\begin{center}

\includegraphics[width=1.0\textwidth]{simulation_1.png} 
\caption{Snapshot of the Simulation}
\label{simulation}
\end{center}
\end{figure}
\clearpage
 The following figure shows different agents doing their defending jobs, where a worker ant is killing an enemy ant attacking the nest, while a discovery ant is stunning an enemy ant approaching the nest.
\begin{figure}[htp]
\begin{center}

\includegraphics[width=1.0\textwidth]{simulation_6.png} 
\caption{Snapshot of the Simulation}
\label{simulation}
\end{center}
\end{figure}
\clearpage
 The following figure shows agents attacking an enemy ant.
\begin{figure}[htp]
\begin{center}

\includegraphics[width=1.0\textwidth]{simulation_4.png} 
\caption{Snapshot of the Simulation}
\label{simulation}
\end{center}
\end{figure}

\clearpage
 The following figure shows agents gathering materials to repair the nest.
\begin{figure}[htp]
\begin{center}

\includegraphics[width=1.0\textwidth]{simulation_5.png} 
\caption{Snapshot of the Simulation}
\label{simulation}
\end{center}
\end{figure}